1. Field of Invention
The present invention relates to noise reduction in broadband communication, specifically to a blind adaptive filter for narrowband interference cancellation, and more specifically to a blind adaptive filter for narrowband interference cancellation which adjusts filter parameters according to the error information obtained after the processing of filtered output signal from the adaptive filter and the delayed output signal.
2. Description of Related Arts
The transport performance of a wideband mobile communication system can be seriously degraded by narrowband interferences from the same channel or from adjacent channels in both analog and digital systems. Noise filtering is the most essential part for any electronic information processing. It could be adopted to extract desired signals from complicated signals and cancel noise or interference at the same time. In narrowband frequency filtering, signals of specific frequency band are expected to minimally attenuate while other undesired signals are greatly attenuated to prevent undesired signals going through. Frequency filter circuit in the electronic system which has been widely applied in communication systems is usually very complicated and the product performance is directly determined by the performance of filters. Therefore, many countries have paid attention to the theory research and product development of filters.
If the interference is weak enough that the synchronization can still be achieved, a number of methods could be applied combined with data to enhance desired signals, to suppress frequency based interference, such as traditional forward or feedback equalization, soft demapper, iterative equalization, and turbo equalization, with significant performance improvement. Discussion of these methods can be found, for example, in publications on Code division multiple access (CDMA) and Orthogonal frequency-division multiplexing (OFDM) system. CDMA is a form of spread-spectrum signaling, since the modulated coded signal has a much higher data bandwidth than the data being communicated. OFDM has developed into a popular scheme for wideband digital communication, whether wireless or over copper wires, used in applications such as digital television and audio broadcasting, wireless networking and broadband internet access. Unfortunately, when interference increases beyond a critical level, signal synchronization fails.
In the past thirty years, many different circuits or systems have been proposed for enhancing desired signals, or separating/canceling noise or different signal frequencies affecting a desired signal. In these methods mentioned above, circuits with different architectures have different abilities to suppress or remove the interference. Although some methods that use the training sequence or some known pilots are effective, the blind ones seem more attractive. A promising technique to reduce narrowband noise is the blind adaptive filtering method for receivers of communication systems. One kind of the new blind adaptive filtering methods applies a notch filter to remove the interference after obtaining its centre frequency. In this technique, interference signal tracking is based on spectrum power density analysis, often acquired through techniques such as Fast Fourier Transform (FFT). However, this method has the least robustness in tracking the change of the interference, even when FFT is performed frequently.
Another blind adaptive filtering method applies adaptive signal processing. An adaptive filter belongs to the category of modern filters, compared with a fixed filter. The fixed filter belongs to the category of classical filters and has fixed filter frequencies. Compared with the fixed filter, the adaptive filter has more extensively applicable scope because the frequency of adaptive filter can be adjusted by the filter itself to meet changing conditions of input signals. Without any prior knowledge about signals or noise, the adaptive filter could automatically adjust the filter parameters of this moment according to the filter parameters obtained a moment ago to adapt to the statistical properties of unknown or random changing signals or noise and achieve optimal filtering. The adaptive filter is a Wiener filter that self-adjusts its parameters according to an optimizing algorithm to meet changing conditions of input signals.
A typical adaptive filter includes two parts: a filter circuitry and an adaptive algorithm to optimize the filter coefficients. The filter circuitry can adopt a Finite Impulse Filter (FIR) circuit or an Infinite Impulse Response (IIR) filter circuit. An FIR filter has a number of useful properties, such as an inherent stability, simplicity, and no feedback required, which sometimes make it preferable to an IIR filter.
FIG. 1 is a block diagram of a conventional adaptive filter. Input signal x(n) 101 is sent into the Delay Unit 110 and the Adaptive Filter 130. An Error Calculation Unit 120 compares the delayed signal 115 and the output signal Y(n) 135 to obtain error information e(n) 125. Error information e(n) 125 is then sent to the Adaptive Filter 130 for adjusting filter parameters, shown below:
                              y          ⁡                      (            n            )                          =                              ∑            i                    ⁢                                                    h                n                            ⁡                              (                i                )                                      ⁢                          x              ⁡                              (                                  n                  -                  i                                )                                                                        (        1        )                                                      h            n                    ⁡                      (            i            )                          =                                            h                              n                -                1                                      ⁡                          (              i              )                                +                      μ            ·                          conj              ⁡                              (                                  x                  ⁡                                      (                    i                    )                                                  )                                      ·                          e              ⁡                              (                i                )                                                                        (        2        )                                                      e            ⁡                          (              i              )                                =                                    x              ⁡                              (                                  n                  -                  D                                )                                      -                          y              ⁡                              (                n                )                                                    ,                            (        3        )            where hn(i) is the transfer function, μ is a constant, conj is conjugation function, D is the delay length. Error function e(i) is minimized through the adjustment.
FIG. 2 is a schematic view of a conventional adaptive filter (prior art). An adaptive filter designed according to specified architecture usually has an adaptive algorithm meeting a certain criteria. The algorithm of adaptive filer always adopts various criteria as algorithms basis. In general, there are two criteria: the Least Mean Square (LMS) criterion and the Least Squares (LS) criterion. The Least Mean Square algorithm minimizes the mean square value of the output error sequence e(i)=x(n−D)−y(n), and adjusts weight coefficients according to the criterion.
A number of references for adaptive filters, such as U.S. Pat. Nos. 4,052,559, 4,238,746, 005,325,204, 4,673,982, 4,524,424, 4,420,815, and 6,976,044, the Proceeding of the IEEE, vol. 63, no. 12, December 1975, pp. 1692-1716, and “Adaptive noise canceling” by Widrow et. al. relate to the least mean square approximation to the Wiener-Hoff filter. The above methods use IIR or FIR filters which remove interference and update filter coefficients by the cross-correlation between the filter output and the input replica's delayed signal. However, the optimization convergence speed is very slow and only interference with a given spectral shape is removed. Therefore, these techniques can not apply to more general situations, for example, in cases of frequency hopping or fast shifting centre frequency.
Therefore, an adaptive filter with better performance and faster convergence speed is desired.